Linear transformation, isomorphic

In summary, the conversation discusses proving that a linear transformation L: Mn,n \rightarrow Mn,n given by L(A) = AB, where B is an invertible n x n matrix, is an isomorphism by showing that it is both onto and one-to-one. Two approaches are suggested, one using the theorem that states a linear transformation is one-to-one if and only if it is onto, and the other directly proving it is an isomorphism. The significance of A being invertible is that, in general, a matrix times a non-zero vector could be the zero vector. To prove one-to-one, the kernel of L must be shown to be {0}.
  • #1
karnten07
213
0

Homework Statement



Let B be an invertible n x n matrix. Prove that the linear transformation L: Mn,n [tex]\rightarrow[/tex] Mn,n given by L(A) = AB, is an isomorphism.

The Attempt at a Solution



I know to show it is an isomorphism i need to show that L is both onto and one-to-one.

By the theorem that says:

Let T:V[tex]\rightarrow[/tex]W be a linear transformation with vector spaces V and W both of dimension n. Then T is one-to-one if and only if it is onto.

To prove both conditions needed for an isomorphism i can just prove it is one-to-one as in this case, 'V' and 'W' are the same dimension and so proving L is one-to-one also proves it is onto.

To prove it is one-to-one, i need to determine the kernel of L and show that it is {0}. To do this i need to use the fact that B is an invertible n x n matrix and L(A)=AB.

I need some guidance on how to use these features to show the kernel of L os {0}?
Thanks in advance
 
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  • #2
well suppose L(A) = L(C), so AB = CB, then ...some stuff... implies A = C, so L is 1-1, you can fill in the missing step

your way, suppose L(A) = 0, so AB = 0, so ...some stuff... implies A = 0, so kerL = {0} so it's 1-1, again you can fill in the missing stepnote the missing step is the same in both approaches
 
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  • #3
ircdan said:
well suppose L(A) = L(C), so AB = CB, then ...some stuff... implies A = C, so L is 1-1, you can fill in the missing step

your way, suppose L(A) = 0, so AB = 0, so ...some stuff... implies A = 0, so kerL = {0} so it's 1-1, again you can fill in the missing step


note the missing step is the same in both approaches

Does it have something to do with the fact that the rows and columns of the invertible matrices are linearly independant and so the kernel must be 0 because of this? Or do i have to use notation to do with the linear transformation conditions? Any more help would be greatly appreciated. Thanks
 
  • #4
karnten07 said:
I know to show it is an isomorphism i need to show that L is both onto and one-to-one.
You're overthinking it. In this case, I think it would be easier to directly prove it's an isomorphism, rather than use that indirect method.
 
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  • #5
If Ax= 0 and A is invertible, then x= ?
 
  • #6
HallsofIvy said:
If Ax= 0 and A is invertible, then x= ?

I assume that x =0, but what is the significance of A being invertible?
 
  • #7
karnten07 said:
I assume that x =0, but what is the significance of A being invertible?

you don't assume that x=0. remember, you need to show that IF Ax = 0 THEN x=0. the significance of A being invertible is that, in general, a matrix times a non-zero vector could be the zero vector. for example, if B = [itex] \left(\begin{array}{cc} 1 & 0 \\ 0 & 0 \end{array}\right)[/itex] and [itex]x = \left(\begin{array}{c} 0 \\ 1 \end{array}\right)[/itex] then Bx = 0. notice, of course, that B is not invertible though.
 
  • #8
If Ax= 0, and A is invertible, how would you solve the equation? If you don't know what A being invertible has to do with the solution to this equation, then you need to go back and review the basics of linear transformations.
 

What is a linear transformation?

A linear transformation is a function that maps one vector space to another in a way that preserves the vector addition and scalar multiplication operations. In other words, the output of a linear transformation will always be a linear combination of the input vectors.

What is an isomorphism?

An isomorphism is a special type of linear transformation that is both one-to-one and onto, meaning that it is bijective. This means that every element in the output vector space has a unique corresponding element in the input vector space, and vice versa.

What is the significance of isomorphic linear transformations?

Isomorphic linear transformations are important because they preserve the structure and properties of the original vector space. This means that any mathematical properties or relationships that hold in the input vector space will also hold in the output vector space.

How do you determine if two vector spaces are isomorphic?

To determine if two vector spaces are isomorphic, you can check if there exists an isomorphism between them. This can be done by finding a linear transformation that is both one-to-one and onto. Alternatively, you can also check if the dimensions of the two vector spaces are equal.

What are some real-world applications of linear transformations and isomorphisms?

Linear transformations and isomorphisms have many applications in various fields such as physics, engineering, and computer science. Some examples include image and signal processing, data compression, and system control. They also play a crucial role in abstract algebra and functional analysis.

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